At-sea distribution of female southern elephant seals relative to variation in ocean surface properties
Bibliographic record
Abstract
Abstract We examined the relationships between physical oceanography (sea surface temperature – SST, sea surface height anomaly – SSH, ocean colour – OC, bathymetry – BA, sea-ice concentration – SI, and their associated gradients) and the foraging distribution (time at sea) of female southern elephant seals using generalized linear and generalized additive models (GLM and GAM). Using data from 28 separate foraging trips (22 unique individuals) over two years, we found that during the post-lactation trips (summer), the best GLM demonstrated a negative relationship between time at sea and SST and BA, but a positive relationship with SST gradient and SSH. During the post-moult (winter) trips, there was a negative relationship with OC gradient, SSH, and BA. The best post-lactation GAM identified a positive relationship with OC gradient, negative relationships with OC and SST gradient, and a non-linear relationship with SST. For the post-moult trip there was a negative relationship with OC, SST, BA and BA gradient, and a positive relationship with SST gradient. The relationship between the predicted time and observed time at sea was significant only for the post-lactation GAM, although predictability was low. That SST and its gradient predicted a small, but significant proportion of the variation in time at sea is indicative of the frontal zones within this area that are generally more biologically productive than surrounding regions. It appears that coarse-scale oceanographic configuration influences foraging behaviour in southern elephant seals only subtly. Nonetheless, some of the mechanisms influencing predator foraging are congruent with expectations of distribution of marine food resources at coarse spatial scales.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".